Artificial intelligence (AI) has grown in presence in asset management and has
revolutionized the sector in many ways. It has improved portfolio management, trading, and …

Abstract Machine learning (ML) methods are attracting considerable attention among
academics in the field of finance. However, it is commonly perceived that ML has not …

The emerging literature suggests that machine learning (ML) is beneficial in many asset
pricing applications because of its ability to detect and exploit nonlinearities and interaction …

This new edited volume consists of a collection of original articles written by leading
financial economists and industry experts in the area of machine learning for asset …

This chapter shows the use of a Bayesian network to model transaction costs on US equity
markets using ANcerno data, a large database of asset managers' instructions. The trading …

Gradient boosting machines (GBMs) based on decision trees consistently demonstrate state-
of-the-art results on regression and classification tasks with tabular data, often outperforming …

Bayesian neural networks (BNN) promise to combine the predictive performance of neural
networks with principled uncertainty modeling important for safety-critical applications and …

This chapter presents a methodology that uses supervised learning techniques, in particular
“random forests” and “gradient boosting trees”, to assess the probability of success on trade …

Linkages across different asset classes and relationships of innovative financial products
such as cryptocurrencies and global macroeconomic events have made price and return …

The development in the area of machine learning is outlined in this paper. Machine learning
has evolved as a new solution in outcome new and accurate decision in large data …